Abstract :
A hierarchical learning structure, combining a randomly-placed local window, a self-organising map and a local-voting scheme, has been developed for the unsupervised segmentation of textured images, which are modelled by Markov random fields. The system learns to progressively estimate model parameters, and hence classify the various textured regions. A globally correct segregation has consistently been obtained during extensive experiments on both synthetic and natural textured images
Keywords :
Markov processes; hierarchical systems; image segmentation; image texture; neural nets; self-organising feature maps; unsupervised learning; Markov random fields; classification; globally correct segregation; hierarchical learning structure; hierarchical neural structure; local-voting scheme; model parameter estimation; randomly-placed local window; self-organising map; textured images; unsupervised segmentation;
